We argue that the weakness of existing SAR image change detection approaches mainly comes from two aspects: One is the correlation of features from different positions fails to be modeled effectively. The other one lies in the intrinsic speckle noise in SAR images. To tackle the aforementioned issues, a Multiscale Capsule Network (Ms-CapsNet) is proposed to extract the discriminative information between multitemporal SAR images. The proposed Ms-CapsNet has similar structure with the Capsule Network [1]} without the multiscale operator and the Adaptive Fusion Convolution (AFC) module. The Ms-CapsNet provides a group of instantiation parameters to capture features from different positions. To tackle the problem of speckle noise, the AFC module is designed to convert pixel intensities to activities of the local features. Accordingly, local features become noise robustness. Extensive experiments on three real datasets are conducted to show the superiority of our proposed method over four state-of-the-art works.
